Related papers: Deep Learning for Anomaly Detection: A Review

Deep Learning for Anomaly Detection: A Review

URL: http://arxiv.org/abs/2007.02500v3
Date: Sat, 5 Dec 2020 04:53:35 GMT
Title: Deep Learning for Anomaly Detection: A Review
Authors: Guansong Pang, Chunhua Shen, Longbing Cao, Anton van den Hengel
Abstract summary: This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges.
Score: 150.9270911031327
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

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